Nowadays, several approaches are dealing with this task, for instance those exploiting temporal cues [1]}, [2]},[3]}, motion [4]}, [5]}, [6]}, action recognition [7]}, [8]}, [9]}, people trajectories [10]}, [11]}, events detection [12]}, [2]}, [14]}, optical flow [15]}, [16]}, audio [17]}, [18]}, [19]}, speech recognition [20]}, to name a few.
As it will show in Section REF  the most fundamental approaches are based on several deep learning architectures, and most of the works are designed as an encoder-decoder model for visual and text data.
Another essential aspect presented in many of the analyzed approaches is the well-known attention mechanism that was designed to improve the performance of the encoder-decoder model, specifically on the machine translation task.
